Volume 6 Issue 1 ( March 2019 )

This paper introduces various smart ventilation methods for energy conservation in buildings, with a focus on occupant-based demand-controlled ventilation. An occupancy estimation algorithm is developed using a Bayesian Markov chain Monte Carlo algorithm based on indoor carbon dioxide concentrations. Experiments are conducted to control the outdoor airflow rate in real time according to the estimated number of occupants. Six different ventilation schemes are tested and compared with the ventilation standard of the American Society of Heating, Refrigerating, and Air-Conditioning Engineers. Our results show that occupant-based demand-controlled ventilation is more effective compared to other control schemes in terms the total ventilation air volume needed. The real-time ventilation control algorithm is applied successfully without any recursive problems. The occupancy estimation algorithm needs to be developed further to improve the estimation accuracy and reduce time delays.

Keywords: smart ventilation, occupancy estimation, Bayesian method, carbon dioxide concentration, demand-controlled ventilation